100 citations · 192 across the 13 of their papers we have counts for
4 papers · 1 filter
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Yongchan Kwon, Eric Wu, Kevin Wu +1
Quantifying the impact of training data points is crucial for understanding the outputs of machine learning models and for improving the transparency of the AI pipeline. The influe…
OpenDataVal: a Unified Benchmark for Data Valuation
Kevin Fu Jiang, Weixin Liang, James Zou +1
Assessing the quality and impact of individual data points is critical for improving model performance and mitigating undesirable biases within the training dataset. Several data v…
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value
Yongchan Kwon, James Zou
Data valuation is a powerful framework for providing statistical insights into which data are beneficial or detrimental to model training. Many Shapley-based data valuation methods…
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations
Weixin Liang, Yining Mao, Yongchan Kwon +2
Understanding the performance of machine learning (ML) models across diverse data distributions is critically important for reliable applications. Despite recent empirical studies…